OpenRaiser/Intern-Atlas huggingface dataset:
Automated Personnel Selection for Software Engineers Using LLM-Based Profile Evaluation
Venue: arXiv cs.SE 2024
Year: 2024

Organizational success in todays competitive employment market depends on choosing the right staff. This work evaluates software engineer profiles using an automated staff selection method based on advanced natural language processing (NLP) techniques. A fresh dataset was generated by collecting LinkedIn profiles with important attributes like education, experience, skills, and self-introduction. Expert feedback helped transformer models including RoBERTa, DistilBERT, and a customized BERT variation, LastBERT, to be adjusted. The models were meant to forecast if a candidate's profile fit the selection criteria, therefore allowing automated ranking and assessment. With 85% accuracy and an F1 score of 0.85, RoBERTa performed the best; DistilBERT provided comparable results at less computing expense. Though light, LastBERT proved to be less effective, with 75% accuracy. The reusable models provide a scalable answer for further categorization challenges. This work presents a fresh dataset and technique as well as shows how transformer models could improve recruiting procedures. Expanding the dataset, enhancing model interpretability, and implementing the system in actual environments will be part of future activities.
Relational Weight Optimization for Enhancing Team Performance in Multi-Agent Multi-Armed Bandits
Venue: arXiv cs.MA 2024
Year: 2024

We introduce an approach to improve team performance in a Multi-Agent Multi-Armed Bandit (MAMAB) framework using Fastest Mixing Markov Chain (FMMC) and Fastest Distributed Linear Averaging (FDLA) optimization algorithms. The multi-agent team is represented using a fixed relational network and simulated using the Coop-UCB2 algorithm. The edge weights of the communication network directly impact the time taken to reach distributed consensus. Our goal is to shrink the timescale on which the convergence of the consensus occurs to achieve optimal team performance and maximize reward. Through our experiments, we show that the convergence to team consensus occurs slightly faster in large constrained networks.
Estimating Neural Network Robustness via Lipschitz Constant and Architecture Sensitivity
Venue: arXiv cs.LG 2024
Year: 2024

Ensuring neural network robustness is essential for the safe and reliable operation of robotic learning systems, especially in perception and decision-making tasks within real-world environments. This paper investigates the robustness of neural networks in perception systems, specifically examining their sensitivity to targeted, small-scale perturbations. We identify the Lipschitz constant as a key metric for quantifying and enhancing network robustness. We derive an analytical expression to compute the Lipschitz constant based on neural network architecture, providing a theoretical basis for estimating and improving robustness. Several experiments reveal the relationship between network design, the Lipschitz constant, and robustness, offering practical insights for developing safer, more robust robot learning systems.
Continuous Evolution of Digital Twins using the DarTwin Notation
Venue: arXiv cs.SE 2024
Year: 2024

Despite best efforts, various challenges remain in the creation and maintenance processes of digital twins (DTs). One of those primary challenges is the constant, continuous and omnipresent evolution of systems, their user's needs and their environment, demanding the adaptation of the developed DT systems. DTs are developed for a specific purpose, which generally entails the monitoring, analysis, simulation or optimization of a specific aspect of an actual system, referred to as the actual twin (AT). As such, when the twin system changes, that is either the AT itself changes, or the scope/purpose of a DT is modified, the DTs usually evolve in close synchronicity with the AT. As DTs are software systems, the best practices or methodologies for software evolution can be leveraged. This paper tackles the challenge of maintaining a (set of) DT(s) throughout the evolution of the user's requirements and priorities and tries to understand how this evolution takes place. In doing so, we provide two contributions: (i) we develop DarTwin, a visual notation form that enables reasoning on a twin system, its purposes, properties and implementation, and (ii) we introduce a set of architectural transformations that describe the evolution of DT systems. The development of these transformations is driven and illustrated by the evolution and transformations of a family home's DT, whose purpose is expanded, changed and re-prioritized throughout its ongoing lifecycle. Additionally, we evaluate the transformations on a lab-scale gantry crane's DT.
Mitigating Challenges in Ethereum's Proof-of-Stake Consensus: Evaluating the Impact of EigenLayer and Lido
Venue: arXiv cs.CR 2024
Year: 2024

The transition of Ethereum from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism introduces a transformative approach to blockchain validation, offering enhanced scalability, energy efficiency, and security. However, this shift also presents significant challenges, including high barriers to becoming a validator, restrictions on the liquidity of staked Ether (ETH), and the risk of centralization due to staking pool dynamics. This paper addresses these challenges by exploring two innovative solutions: EigenLayer and Lido. EigenLayer is a middleware solution enabling restaking, allowing validators to secure multiple protocols and thereby increasing decentralization and profitability. Lido, a liquid staking protocol, simplifies participation by issuing stETH tokens that retain liquidity, allowing users to earn rewards without long-term lock-up constraints. This paper provides a detailed analysis of how these technologies mitigate key PoS challenges, reduce validator entry barriers, unlock staked capital, and improve decentralization. We conclude with an evaluation of the combined potential of EigenLayer and Lido to foster a more resilient and inclusive Ethereum ecosystem, setting the stage for further advancements in decentralized finance.
Web Scraping for Research: Legal, Ethical, Institutional, and Scientific Considerations
Venue: arXiv cs.CY 2024
Year: 2024

Scientists across disciplines often use data from the internet to conduct research, generating valuable insights about human behavior. However, as generative AI relying on massive text corpora becomes increasingly valuable, platforms have greatly restricted access to data through official channels. As a result, researchers will likely engage in more web scraping to collect data, introducing new challenges and concerns for researchers. This paper proposes a comprehensive framework for web scraping in social science research for U.S.-based researchers, examining the legal, ethical, institutional, and scientific factors that researchers should consider when scraping the web. We present an overview of the current regulatory environment impacting when and how researchers can access, collect, store, and share data via scraping. We then provide researchers with recommendations to conduct scraping in a scientifically legitimate and ethical manner. We aim to equip researchers with the relevant information to mitigate risks and maximize the impact of their research amidst this evolving data access landscape.
Graph-Augmented Relation Extraction Model with LLMs-Generated Support Document
Venue: arXiv cs.CL 2024
Year: 2024

This study introduces a novel approach to sentence-level relation extraction (RE) that integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs) to generate contextually enriched support documents. By harnessing the power of LLMs to generate auxiliary information, our approach crafts an intricate graph representation of textual data. This graph is subsequently processed through a Graph Neural Network (GNN) to refine and enrich the embeddings associated with each entity ensuring a more nuanced and interconnected understanding of the data. This methodology addresses the limitations of traditional sentence-level RE models by incorporating broader contexts and leveraging inter-entity interactions, thereby improving the model's ability to capture complex relationships across sentences. Our experiments, conducted on the CrossRE dataset, demonstrate the effectiveness of our approach, with notable improvements in performance across various domains. The results underscore the potential of combining GNNs with LLM-generated context to advance the field of relation extraction.
The Trail Making Test in Virtual Reality (TMT-VR): The Effects of Interaction Modes and Gaming Skills on Cognitive Performance of Young Adults
Venue: arXiv cs.HC 2024
Year: 2024

Virtual Reality (VR) is increasingly used in neuropsychological assessments due to its ability to simulate real-world environments. This study aimed to develop and evaluate the Trail Making Test in VR (TMT-VR) and investigate the effects of different interaction modes and gaming skills on cognitive performance. A total of 71 young female and male adults (aged 18-35) with high and low gaming skills participated in this study. Participants completed the TMT-VR using three interaction modes as follows: eye tracking, head movement, and controller. Performance metrics included task completion time and accuracy. User experience, usability, and acceptability of TMT-VR were also examined. Results showed that both eye tracking and head movement modes significantly outperformed the controller in terms of task completion time and accuracy. No significant differences were found between eye tracking and head movement modes. Gaming skills did not significantly influence task performance using any interaction mode. The TMT-VR demonstrates high usability, acceptability, and user experience among participants. The findings suggest that VR-based assessments can effectively measure cognitive performance without being influenced by prior gaming skills, indicating potential applicability for diverse populations.
LBurst: Learning-Based Robotic Burst Feature Extraction for 3D Reconstruction in Low Light
Venue: arXiv cs.CV 2024
Year: 2024

Drones have revolutionized the fields of aerial imaging, mapping, and disaster recovery. However, the deployment of drones in low-light conditions is constrained by the image quality produced by their on-board cameras. In this paper, we present a learning architecture for improving 3D reconstructions in low-light conditions by finding features in a burst. Our approach enhances visual reconstruction by detecting and describing high quality true features and less spurious features in low signal-to-noise ratio images. We demonstrate that our method is capable of handling challenging scenes in millilux illumination, making it a significant step towards drones operating at night and in extremely low-light applications such as underground mining and search and rescue operations.
Prosody as a Teaching Signal for Agent Learning: Exploratory Studies and Algorithmic Implications
Venue: arXiv cs.LG 2024
Year: 2024

Agent learning from human interaction often relies on explicit signals, but implicit social cues, such as prosody in speech, could provide valuable information for more effective learning. This paper advocates for the integration of prosody as a teaching signal to enhance agent learning from human teachers. Through two exploratory studies--one examining voice feedback in an interactive reinforcement learning setup and the other analyzing restricted audio from human demonstrations in three Atari games--we demonstrate that prosody carries significant information about task dynamics. Our findings suggest that prosodic features, when coupled with explicit feedback, can enhance reinforcement learning outcomes. Moreover, we propose guidelines for prosody-sensitive algorithm design and discuss insights into teaching behavior. Our work underscores the potential of leveraging prosody as an implicit signal for more efficient agent learning, thus advancing human-agent interaction paradigms.
Microsecond-scale Dynamic Validation of Idempotency for GPU Kernels
Venue: arXiv cs.OS 2024
Year: 2024

We discovered that a GPU kernel can have both idempotent and non-idempotent instances depending on the input. These kernels, called conditionally-idempotent, are prevalent in real-world GPU applications (490 out of 547 from six applications). Consequently, prior work that classifies GPU kernels as either idempotent or non-idempotent can severely compromise the correctness or efficiency of idempotence-based systems. This paper presents PICKER, the first system for instance-level idempotency validation. PICKER dynamically validates the idempotency of GPU kernel instances before their execution, by utilizing their launch arguments. Several optimizations are proposed to significantly reduce validation latency to microsecond-scale. Evaluations using representative GPU applications (547 kernels and 18,217 instances in total) show that PICKER can identify idempotent instances with no false positives and a false-negative rate of 18.54%, and can complete the validation within 5 us for all instances. Furthermore, by integrating PICKER, a fault-tolerant system can reduce the checkpoint cost to less than 4% and a scheduling system can reduce the preemption latency by 84.2%.
XRDSLAM: A Flexible and Modular Framework for Deep Learning based SLAM
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we propose a flexible SLAM framework, XRDSLAM. It adopts a modular code design and a multi-process running mechanism, providing highly reusable foundational modules such as unified dataset management, 3d visualization, algorithm configuration, and metrics evaluation. It can help developers quickly build a complete SLAM system, flexibly combine different algorithm modules, and conduct standardized benchmarking for accuracy and efficiency comparison. Within this framework, we integrate several state-of-the-art SLAM algorithms with different types, including NeRF and 3DGS based SLAM, and even odometry or reconstruction algorithms, which demonstrates the flexibility and extensibility. We also conduct a comprehensive comparison and evaluation of these integrated algorithms, analyzing the characteristics of each. Finally, we contribute all the code, configuration and data to the open-source community, which aims to promote the widespread research and development of SLAM technology within the open-source ecosystem.
Towards Reliable Alignment: Uncertainty-aware RLHF
Venue: arXiv cs.AI 2024
Year: 2024

Recent advances in aligning Large Language Models with human preferences have benefited from larger reward models and better preference data. However, most of these methodologies rely on the accuracy of the reward model. The reward models used in Reinforcement Learning with Human Feedback (RLHF) are typically learned from small datasets using stochastic optimization algorithms, making them prone to high variability. We illustrate the inconsistencies between reward models empirically on numerous open-source datasets. We theoretically show that the fluctuation of the reward models can be detrimental to the alignment problem because the derived policies are more overfitted to the reward model and, hence, are riskier if the reward model itself is uncertain. We use concentration of measure to motivate an uncertainty-aware, conservative algorithm for policy optimization. We show that such policies are more risk-averse in the sense that they are more cautious of uncertain rewards. We theoretically prove that our proposed methodology has less risk than the vanilla method. We corroborate our theoretical results with experiments based on designing an ensemble of reward models. We use this ensemble of reward models to align a language model using our methodology and observe that our empirical findings match our theoretical predictions.
Elementary methods for splitting representations of Rook monoids: a gentle introduction to groupoids
Venue: arXiv cs.DM 2024
Year: 2024

We show that the algebra of the coloured rook monoid $R_n^{(r)}$, {\em i.e.} the monoid of $n \times n$ matrices with at most one non-zero entry (an $r$-th root of unity) in each column and row, is the algebra of a finite groupoid, thus is endowed with a $C^*$-algebra structure. This new perspective uncovers the representation theory of these monoid algebras by making manifest their decomposition in irreducible modules.
Neurobench: DCASE 2020 Acoustic Scene Classification benchmark on XyloAudio 2
Venue: arXiv cs.SD 2024
Year: 2024

XyloAudio is a line of ultra-low-power audio inference chips, designed for in- and near-microphone analysis of audio in real-time energy-constrained scenarios. Xylo is designed around a highly efficient integer-logic processor which simulates parameter- and activity-sparse spiking neural networks (SNNs) using a leaky integrate-and-fire (LIF) neuron model. Neurons on Xylo are quantised integer devices operating in synchronous digital CMOS, with neuron and synapse state quantised to 16 bit, and weight parameters quantised to 8 bit. Xylo is tailored for real-time streaming operation, as opposed to accelerated-time operation in the case of an inference accelerator. XyloAudio includes a low-power audio encoding interface for direct connection to a microphone, designed for sparse encoding of incident audio for further processing by the inference core. In this report we present the results of DCASE 2020 acoustic scene classification audio benchmark dataset deployed to XyloAudio 2. We describe the benchmark dataset; the audio preprocessing approach; and the network architecture and training approach. We present the performance of the trained model, and the results of power and latency measurements performed on the XyloAudio 2 development kit. This benchmark is conducted as part of the Neurobench project.
Promoting Reliable Knowledge about Climate Change: A Systematic Review of Effective Measures to Resist Manipulation on Social Media
Venue: arXiv cs.CY 2024
Year: 2024

We present a systematic review of peer-reviewed research into ways to mitigate manipulative information about climate change on social media. Such information may include disinformation, harmful influence campaigns, or the unintentional spread of misleading information. We find that commonly recommended approaches to addressing manipulation about climate change include corrective information sharing and education campaigns targeting media literacy. However, most relevant research fails to test the approaches and interventions it proposes. We locate research gaps that include the lack of attention to large commercial and political entities involved in generating and disseminating manipulation, video- and image-focused platforms, and computational methods to collect and analyze data. Evidence drawn from many studies demonstrates an emerging consensus about policies required to promote reliable knowledge about climate change and resist manipulation.
Case ID detection based on time series data -- the mining use case
Venue: arXiv cs.DB 2024
Year: 2024

Process mining gains increasing popularity in business process analysis, also in heavy industry. It requires a specific data format called an event log, with the basic structure including a case identifier (case ID), activity (event) name, and timestamp. In the case of industrial processes, data is very often provided by a monitoring system as time series of low level sensor readings. This data cannot be directly used for process mining since there is no explicit marking of activities in the event log, and sometimes, case ID is not provided. We propose a novel rule-based algorithm for identification patterns, based on the identification of significant changes in short-term mean values of selected variable to detect case ID. We present our solution on the mining use case. We compare computed results (identified patterns) with expert labels of the same dataset. Experiments show that the developed algorithm in the most of the cases correctly detects IDs in datasets with and without outliers reaching F1 score values: 96.8% and 97% respectively. We also evaluate our algorithm on dataset from manufacturing domain reaching value 92.6% for F1 score.
DynaSplit: A Hardware-Software Co-Design Framework for Energy-Aware Inference on Edge
Venue: arXiv cs.DC 2024
Year: 2024

The deployment of ML models on edge devices is challenged by limited computational resources and energy availability. While split computing enables the decomposition of large neural networks (NNs) and allows partial computation on both edge and cloud devices, identifying the most suitable split layer and hardware configurations is a non-trivial task. This process is in fact hindered by the large configuration space, the non-linear dependencies between software and hardware parameters, the heterogeneous hardware and energy characteristics, and the dynamic workload conditions. To overcome this challenge, we propose DynaSplit, a two-phase framework that dynamically configures parameters across both software (i.e., split layer) and hardware (e.g., accelerator usage, CPU frequency). During the Offline Phase, we solve a multi-objective optimization problem with a meta-heuristic approach to discover optimal settings. During the Online Phase, a scheduling algorithm identifies the most suitable settings for an incoming inference request and configures the system accordingly. We evaluate DynaSplit using popular pre-trained NNs on a real-world testbed. Experimental results show a reduction in energy consumption up to 72% compared to cloud-only computation, while meeting ~90% of user request's latency threshold compared to baselines.
IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking
Venue: arXiv cs.CV 2024
Year: 2024

Multi-Object Tracking (MOT) aims to associate multiple objects across video frames and is a challenging vision task due to inherent complexities in the tracking environment. Most existing approaches train and track within a single domain, resulting in a lack of cross-domain generalizability to data from other domains. While several works have introduced natural language representation to bridge the domain gap in visual tracking, these textual descriptions often provide too high-level a view and fail to distinguish various instances within the same class. In this paper, we address this limitation by developing IP-MOT, an end-to-end transformer model for MOT that operates without concrete textual descriptions. Our approach is underpinned by two key innovations: Firstly, leveraging a pre-trained vision-language model, we obtain instance-level pseudo textual descriptions via prompt-tuning, which are invariant across different tracking scenes; Secondly, we introduce a query-balanced strategy, augmented by knowledge distillation, to further boost the generalization capabilities of our model. Extensive experiments conducted on three widely used MOT benchmarks, including MOT17, MOT20, and DanceTrack, demonstrate that our approach not only achieves competitive performance on same-domain data compared to state-of-the-art models but also significantly improves the performance of query-based trackers by large margins for cross-domain inputs.
Transformers to Predict the Applicability of Symbolic Integration Routines
Venue: arXiv cs.LG 2024
Year: 2024

Symbolic integration is a fundamental problem in mathematics: we consider how machine learning may be used to optimise this task in a Computer Algebra System (CAS). We train transformers that predict whether a particular integration method will be successful, and compare against the existing human-made heuristics (called guards) that perform this task in a leading CAS. We find the transformer can outperform these guards, gaining up to 30% accuracy and 70% precision. We further show that the inference time of the transformer is inconsequential which shows that it is well-suited to include as a guard in a CAS. Furthermore, we use Layer Integrated Gradients to interpret the decisions that the transformer is making. If guided by a subject-matter expert, the technique can explain some of the predictions based on the input tokens, which can lead to further optimisations.